جست‌وجوی استاد و پژوهشگر

John Bochanskiمشاهده پروفایل

دانشیار

• Astronomy
• Physics
• Galactic Structure
• +۴ مورد دیگر

John Bochanski is an Associate Professor of Computer Science & Physics at Rider University, specializing in using large astronomical surveys to study the formation and evolution of the Milky Way galaxy. He is actively involved in major astronomical projects including the Sloan Digital Sky Survey (SDSS) and the Large Synoptic Survey Telescope (LSST) Science Collaborations. His educational background includes a Ph.D. in Astronomy from the University of Washington (2008), an M.S. in Astronomy from the same institution, and a B.S. in Astronomy & Astrophysics from Villanova University, where he graduated magna cum laude. Bochanski's research focuses on galactic structure and kinematics using cool, red stars as tracers. He employs large surveys like SDSS and Gaia to study two key regimes in the Milky Way: using cool red dwarfs to examine galactic structure near the Sun, and utilizing brighter red giants to investigate the distant Milky Way halo. His work is particularly relevant to upcoming surveys with the Nancy Grace Roman Space Telescope and the Vera Rubin Observatory. Supported by the LSST Discovery Alliance in cooperation with Princeton University's Department of Astrophysical Sciences, his research spans stellar kinematics, low-mass stars, and advanced data analysis techniques. His publication record shows a consistent focus on galactic structure, brown dwarfs, and large astronomical surveys, with recent work centered on SDSS-V and Gaia data analysis. The trend in his publications demonstrates increasing involvement in large-scale collaborative projects and data-intensive astronomy. Corporation for Science Advancement - Scialog Fellow (2018) Namesake of Main–belt asteroid 141414 Bochanski (2002 AK205) (2012) MIT Spot Award (2009, 2010) Elected to Phi Beta Kappa (2002) Jason Cardelli Memorial Undergraduate Research Award (2002) National Barry M. Goldwater Scholar (2001) Elected to Phi Kappa Phi (2001) Elected to Sigma Pi Sigma Physics (2000) Bochanski has held positions at multiple prestigious institutions including Rider University, Haverford College, Pennsylvania State University, and MIT. His current research is supported by the LSST Discovery Alliance, indicating significant grant funding for his work. He has mentored numerous students through his position as Program Founder & Director of Computer Science at Rider University since 2016. His technical expertise includes working with the FIRE (Magellan FIRE) spectrograph and developing data analysis pipelines for large astronomical surveys. His work with the Sloan Digital Sky Survey and Gaia mission data places him at the forefront of modern galactic astronomy research.

• Multi-Object Tracking
• Signal Processing
• Computer Vision
• +۵ مورد دیگر

Dr. Changbeom Shim is a Research Fellow at Curtin University, affiliated with the School of Electrical Engineering, Computing and Mathematical Sciences under the Faculty of Science and Engineering. His role involves advancing research in multi-object tracking, signal processing, and data analysis. He holds an email address at Curtin University and maintains an active Google Scholar profile and personal website. Shim's research focuses on developing advanced algorithms for multi-object tracking using labeled random finite sets (LRFS), with applications in computer vision, bioinformatics, and geospatial systems. Key areas include trajectory estimation, sensor fusion, and scalable computational methods. His work bridges theoretical frameworks with practical tools like CellTrackVis for biological data visualization and SkyFlow for time-series analysis. His publications from 2017 to 2025 reflect a progression from foundational spatial queries in social networks to cutting-edge multi-sensor object tracking and autonomous systems. The 2024 paper on LRFS overviews highlights his leadership in this field. His research emphasizes efficiency, scalability, and cross-disciplinary applications, with contributions to both algorithmic theory and user-facing tools. Shim collaborates across disciplines, addressing challenges in sensor networks, autonomous robotics, and biological cell tracking. His work often integrates statistical methods with computational techniques, yielding innovations in filtering, visualization, and data exploration. Current trends in his publications emphasize multi-scan/multi-sensor systems and real-world applications like geotechnical property estimation and audio-visual source separation.

Michael Grossniklausمشاهده پروفایل

استاد

• Data Management
• Information Systems
• Stream Processing
• +۳ مورد دیگر

Michael Grossniklaus is Professor for Databases and Information Systems at University of Konstanz's Department of Computer and Information Science. His research develops data management and processing technologies supporting data science applications, with emphasis on stream processing, context-aware systems, and web engineering solutions. Key research areas: Continuous query processing for streaming data Context-aware mobile data management Machine learning for database optimization Multi-channel web information systems Semantic web and RDF stream processing Publications demonstrate sustained contributions to database query processing, with recent work focusing on cardinality estimation using geometric deep learning and cloud database optimization. Developed C-SPARQL language extension for continuous RDF stream queries.

Jianjun Yangمشاهده پروفایل

استاد

• Wireless Networking
• Image Processing
• Social Network Analysis
• +۲ مورد دیگر

Dr. Jianjun Yang is Professor of Computer Science in the Mike Cottrell College of Business at the University of North Georgia. His research spans wireless networking, social network analysis, and computational modeling of complex systems. Education: Ph.D. in Computer Science from University of Kentucky, M.S. degrees from University of Kentucky and Fuzhou University. Research Focus: Developing computational models for social network dynamics, public opinion propagation, and emergency management systems. Recent work examines pandemic-related behaviors including panic buying, opinion polarization, and online education platform adoption. Publication Trends: Applies data mining, game theory, and complex network analysis to model social phenomena during crises. Techniques include opinion mining, agent-based simulation, and spatiotemporal data modeling for applications in e-commerce, disaster response, and educational technology.

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Mining
• Database Systems
• Graph Data Management
• +۷ مورد دیگر

Panagiotis Karras is a Professor of Computer Science at the University of Copenhagen and a faculty affiliate at Aarhus University. His research centers on robust and versatile methods for data access, mining, and representation. He has held academic positions at Aalborg University, Skoltech, Rutgers, NUS, the University of Zurich, and DTU, reflecting a broad international academic footprint. His research interests lie at the intersection of database systems, data mining, and machine learning, with a focus on graph data management, data privacy, indexing, influence maximization, and clustering. He aims to develop scalable and efficient algorithms that maintain high utility under privacy and accuracy constraints. His recent publications reveal a strong trend in graph-based methods, including graph alignment (GRASP, FREDE), influence and diffusion modeling, data anonymization, and adaptive indexing. These works appear in premier venues such as VLDB, SIGMOD, KDD, ICDE, and AAAI, indicating sustained high-impact contributions. He has been recognized with the 2008 Young Scientist Award in Physical/Mathematical Science from HKIS. Karras is actively seeking motivated PhD students and has supervised numerous collaborative projects. While specific grant details are not listed, his continuous publication output and affiliations suggest active research funding. His work often involves interdisciplinary collaboration, particularly in graph analysis and privacy-preserving technologies. He has contributed significantly to the development of efficient data synopses, anonymization frameworks (e.g., SABRE, L-opacity), and scalable graph processing systems, often in collaboration with leading researchers in the database and data mining communities.

Aravinda Prasad Sistlaمشاهده پروفایل

استاد

• Formal Methods in Concurrent and Distributed Systems
• Model Checking
• Temporal Logic
• +۷ مورد دیگر

Aravinda Prasad Sistla is a Professor in the Department of Computer Science at the University of Illinois at Chicago. His research focuses on formal methods for verifying concurrent and distributed systems, security analysis, and database management systems. Education: Ph.D. in Computer Science/Applied Mathematics from Harvard University (1983) M.E. in Computer Science from Indian Institute of Science (1976) B.Tech. in Electronics and Communications Engineering from National Institute of Technology, Warangal (1974) Research Interests: He works on static and runtime verification of concurrent and distributed systems, including model checking for deterministic and probabilistic systems, symmetry reductions to address state explosion, and runtime fault detection using Hidden Markov Models. His security research involves model checking access control policies, verifying cryptographic protocols, and preventing confidential information leakage. Earlier work includes query processing in moving object, active, and textual databases.

Dragan H. Stojanovicمشاهده پروفایل

استاد

• Artificial Intelligence
• Edge Computing
• Smart City Systems
• +۳ مورد دیگر

Dr. Dragan H. Stojanovic serves as a Professor at the Faculty of Electronic Engineering, University of Niš, specializing in computer science since his appointment in 2015. His academic journey spans over three decades at the same institution, where he earned all his degrees and now leads research in cutting-edge computational fields. His educational background includes: PhD in Electrical Engineering and Computing (2004, University of Niš) MSc in Electrical Engineering and Computing (1998, University of Niš) BSc in Electrical Engineering and Computing (1993, University of Niš) Stojanovic's research centers on Artificial Intelligence and Edge Computing , with significant contributions to Smart City infrastructures, parallel processing algorithms, and neural network applications. His work bridges theoretical computer science with practical implementations in urban systems, demonstrated through collaborations on 1 national and 2 international research projects . Recent publications (2022-2025) reveal a strategic pivot toward resource-constrained AI deployment across edge-fog architectures, while earlier work (2014-2020) established foundations in geospatial data parallelization and social media analytics. His publication portfolio shows evolving expertise: 2014-2019: Focus on parallel computing (MPI/CUDA) for geospatial and trajectory data 2020-2022: Integration of IoT and social networks into Smart City business processes 2023-2025: Advanced Edge AI optimization for real-time object detection and prediction systems Stojanovic actively contributes to academic knowledge transfer through project leadership and has published 16 journal articles in impact-factor journals. His current research trajectory emphasizes sustainable computing paradigms for urban environments, with emerging work on competency-focused AI education frameworks.

Nikos Pelekisمشاهده پروفایل

مدرس

• Data Mining
• Spatiotemporal Databases
• Mobility Data Analysis
• +۲ مورد دیگر

Nikos Pelekis is a Lecturer at the Department of Statistics and Insurance Science and a researcher at the Information Management Group in the Department of Informatics at the University of Piraeus. His research specializes in mobility data management, spatiotemporal databases, and knowledge discovery from moving objects. Research Focus: Design of trajectory database engines (HERMES) Semantic-aware mobility data mining Privacy-preserving techniques for sensitive trajectory data Key Achievements: Best Paper Award at ER'13 and IEEE ICDM'09 Author of "Mobility Data Management and Exploration" monograph Principal researcher in EU projects including GeoPKDD and MODAP

Claudio SILVESTRIمشاهده پروفایل

دانشیار

• Data Mining
• Privacy in Location-Based Services
• Spatio-Temporal Data Analysis
• +۳ مورد دیگر

Claudio SILVESTRI is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He specializes in Computer Science (INFO-01/A), with a focus on data mining, privacy in location-based services, and spatio-temporal data analysis. His research integrates computer science with environmental and biomedical applications. Teaching Responsibilities include courses on Advanced Data Management (Computer Science) and Geographic Information Systems (Environmental Sciences) at the Master's level across multiple academic years. Research Interests span: Algorithms for privacy protection in location-based services Spatio-Temporal Data Warehouses and trajectory analysis Parallel computing on GPU and cloud platforms Applications in fisheries monitoring and diabetic kidney disease modeling Funding Projects include EU initiatives like H2020 (e.g., DC-ren for kidney disease research) and regional grants (e.g., ADMIN4D on Industry 4.0). Key collaborations involve researchers like Salvatore ORLANDO and Debora SLANZI. He is affiliated with the European Center for Living Technology (ECLT) and the Research Institute for Social Innovation. Office hours are held Wednesdays 2-4 PM by email appointment.

• Maritime Safety
• Deep Learning
• Anomaly Detection
• +۳ مورد دیگر

Dogan Altan serves as a Postdoctoral Fellow at Simula Research Laboratory within the Department of Validation Intelligence for Autonomous Software Systems. His research focuses on developing artificial intelligence solutions for maritime safety and operational efficiency, leveraging advanced machine learning techniques to address complex vessel navigation and performance challenges. His primary research interests include maritime anomaly detection, vessel traffic prediction, and physics-informed neural modeling for shaft power optimization. Altan specializes in integrating sensor data, passage plans, and environmental features into deep learning frameworks to enhance predictive accuracy in maritime contexts. Key methodologies involve transfer learning, trajectory segmentation, and multi-stream anomaly identification systems applicable to both vessel operations and robotic manipulation. Analysis of his publication trends reveals a consistent focus on transforming maritime domain knowledge into robust AI frameworks. Recent work demonstrates innovation in temporal graph analysis for port classification, transformer-based waypoint detection, and physics-guided hybrid models that bridge engineering principles with neural network architectures. His contributions span theoretical algorithm development and practical implementations validated through real-world maritime datasets.

• Computer Vision
• Image Analysis
• Machine Learning
• +۴ مورد دیگر

Dr Shahana Bano is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University, specializing in interdisciplinary applications of Artificial Intelligence. Her research spans biomedical imaging, environmental monitoring, infrastructure analytics, and socio-technical systems through the Machine Vision Research Group and Cybersecurity Research Group. Her educational background includes a PhD in Computer Science and Engineering, M.Tech in Computer Science and Engineering, MSc in Information Systems, and BCA - all completed full-time. Her research philosophy centers on convergence, bringing together diverse data types, technologies, and disciplines to create impactful, adaptive systems that bridge academic innovation with societal relevance. Dr Bano's research interests focus on Computer Vision, Image Analysis, Machine Learning, Data Analytics, Internet of Things, Text to Speech Systems, and Social Media Threat Intelligence. Her recent work demonstrates strong application of these interests to medical diagnostics (CAR-T cell classification), environmental monitoring (geothermal reservoir modeling), infrastructure analytics (pipeline defect detection), and social systems (hate detection in football communities). Her publication record shows consistent output with 32 research outputs from 2015-2025, demonstrating increasing focus on medical and environmental applications of AI in recent years. The work spans theoretical computer vision advancements to practical implementations addressing UN Sustainable Development Goals. Associate Fellow (AFHEA) 2023 from Advance HE Dr Bano actively supervises PhD students and mentors interns on diverse projects. Current PhD supervision includes research on morphological classification of CAR-T cell images for leukemia diagnosis and acoustic emission-based pipeline defect detection. She also guides students on projects involving AR-based navigation, NDVI vegetation mapping, hate detection in football communities, multimodal sensor fusion, and airport runway object detection. Her lab resources are supported through university research groups and collaborations focused on machine vision and cybersecurity applications.

Luigi Santocanaleمشاهده پروفایل

استاد

• Order Theory
• Category Theory
• Universal Algebra
• +۱۲ مورد دیگر

Luigi Santocanale is a Full Professor in Computer Science at Aix-Marseille University, affiliated with the Laboratoire d'Informatique et Systèmes (LIS), UMR CNRS 7020, and member of the LSC research group. He is responsible for the 3rd year of the Licence Informatique, parcours Informatique Mathématique at Aix-Marseille University. His academic career includes previous affiliations with LIRICA@LIS, MOVE@LIF, LABRI, CPSC@UCalgary, BRICS, and UQAM. Professor Santocanale's research spans three interconnected domains: Order Theory, Category Theory and Universal Algebra (including Lattice theory, Topos Theory, and Enriched Category Theory); Combinatorics (focusing on Ordered sets, species theory, and game theory); and Logic in Computer Science (covering Intuitionistic logic, mu-calculus, decision problems for free algebraic objects, semantics of programming languages, and algebraic structures of distributed computing). His current research directions include lattice theory applications to databases, logics with fixed points, ordered sets in combinatorics, and circular proofs in mu-bicomplete categories. He has served as project coordinator for the ANR SOAPDC (Order Structures and Applications to Distributed, Parallel and Concurrent Computing) project and has been involved in numerous other research initiatives including LAMBDACOMB, RECIPROG, TICAMORE, MFL, TRECOLOCOCO, CHOCO, and TAGADA. Professor Santocanale is actively engaged with the academic community as a Program Committee member for major conferences including RAMiCS 2024 and TACL 2024. His significant contributions include: Decidability of the equational theory of relational lattices with applications to database query optimization Characterization of closure ordinals in modal mu-calculus Work on equational theories of permutation lattices (Permutohedra) and associahedra Development of circular proof systems for mu-bicomplete categories Professor Santocanale has organized and participated in the Marseille Lattice Working Group and has collaborated extensively with researchers including Fred Wehrung, M. J. Gouveia, and Nathalie Caspard. His work bridges theoretical computer science, mathematical logic, and algebraic structures, with implications for programming language semantics and distributed computing.

• Mobility Data Analytics
• Big Data
• Machine Learning
• +۸ مورد دیگر

Dr. Omid Isfahani Alamdari serves as Assistant Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, bringing expertise in mobility data analytics and big data systems developed through international research experience. Education PhD in Computer Science, University of Pisa, Italy MSc in Computer Engineering - Software (Distributed Systems), Iran University of Science and Technology BSc in Computer Engineering - Software, Urmia University, Iran Research Focus His research centers on developing efficient trajectory analysis methods and advanced indexing techniques for massive mobility datasets. Key applications include sustainable transportation solutions (electric vehicle adoption, carpooling optimization) and explainable event prediction systems combining historical patterns with real-time data streams. He actively explores generative AI applications for mobility challenges and time series analysis. Publication Trends Publications from 2018-2023 reveal consistent contributions to transportation analytics, with dominant themes in trajectory processing (40%), sustainable mobility (30%), and prediction systems (30%). His work appears in top transportation venues (IEEE Transactions on ITS) and data science conferences (IEEE BigData, SIGSPATIAL), featuring strong international collaboration patterns. Academic Activities Teaching: Agile Software Development, Python for Data Analytics, SQL Databases, and Data Analytics Case Studies Research: Currently leads EU-inspired projects on sustainable mobility and event prediction Specialization: Trajectory analysis, spatio-temporal indexing, EV simulation, graph embedding